Rank-based estimation for autoregressive moving average time series models

Beth Andrews*

*Corresponding author for this work

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

We establish asymptotic normality and consistency for rank-based estimators of autoregressive-moving average model parameters. The estimators are obtained by minimizing a rank-based residual dispersion function similar to the one given by L.A. Jaeckel [Ann. Math. Stat. Vol. 43 (1972) 1449-1458]. These estimators can have the same asymptotic efficiency as maximum likelihood estimators and are robust. The quality of the asymptotic approximations for finite samples is studied via simulation.

Original languageEnglish (US)
Pages (from-to)51-73
Number of pages23
JournalJournal of Time Series Analysis
Volume29
Issue number1
DOIs
StatePublished - Jan 1 2008

Keywords

  • Autoregressive moving average models
  • Rank estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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